Defining immunosuppression phenotypes using machine learning

Are you passionate about AI’s potential to solve real-world challenges? Join the Healthcare Ecosystems theme of the AI for Collective Intelligence Hub as a PhD student and contribute to cutting-edge research aimed at using large-scale health databases to improve collaborative decision making between clinicians, patients and policy makers.

Overview of the Research:

This fully-funded 3-year PhD project will use machine learning techniques to devise more tailored vaccination policies to protect immunosuppressed individuals. Immunosuppression is a heterogenous state, reflecting multiple underlying causes. Different factors (such as kidney disease) may play different roles in infection risk according to the individual context (such as the cause, severity or treatment of the kidney disease). Understanding immunosuppression is critical for managing individual patients’ risk of infection and for policy-makers’ response to epidemics. At present, vaccine policy tends to combine individuals with different types and causes of immunosuppression, or to use checklists of conditions which do not vary according to the individual context.

You will use machine learning techniques to define meaningful subgroups of immunosuppression, grouping individuals according to their risk of infection. Markers of immunosuppression will be identified in anonymised patient data from electronic health records. Supervised learning (such as random forest modelling) will be used to identify predictors of infection, followed by unsupervised learning (such as different types of cluster analysis) to define groups who share an infection risk. This will generate methods to define and group immunosuppressed populations, supporting more individualised recommendations on preventing infection and offering the opportunity for more nuanced vaccination policy.

You will work alongside a growing network of PhD students and postdocs in health data science at the University of Bath and benefit from a wide range of development and networking activities across the other nodes of the Hub at Bristol, Cardiff, Exeter, Glasgow, UCL and Ulster. This PhD is an excellent opportunity to build on your analytics skills in a high-impact project, preparing you for a future career in health research in academia or industry.

Project keywords: Clustering, electronic health records, immunosuppression, vaccine policy

Candidate Requirements:

Applicants should hold, or expect to receive, a First Class or high Upper Second Class UK Honours degree (or the equivalent) in a relevant subject such as health data science, epidemiology, statistics, computer science or clinical training. A master’s level qualification and/or relevant clinical experience would also be advantageous.  Experience with data analysis (using a language such as R, Python or Stata), ideally through independent project work, is essential.

Enquiries and Applications:

Informal enquiries are encouraged and should be directed to Dr Theresa Smith [email protected]

Formal applications should be submitted via the University of Bath’s online application form for a PhD in Biology prior to the closing date of this advert.

IMPORTANT:

When completing the application form:

1.      In the Funding your studies section, select ‘University of Bath LURS’ as the studentship for which you are applying.

2.      In the Your PhD project section, quote the project title of this project and Helen McDonald as the lead supervisor. 

Failure to complete these two steps will cause delays in processing your application and may cause you to miss the deadline.

More information about applying for a PhD at Bath may be found on our website.

PLEASE BE AWARE: Applications for this project may close earlier than the advertised deadline if a suitable candidate is found. We therefore recommend that you contact the lead supervisor prior to applying and submit your formal application as early as possible.

Equality, Diversity and Inclusion:

We value a diverse research environment and aim to be an inclusive university, where difference is celebrated and respected. We welcome and encourage applications from under-represented groups.

If you have circumstances that you feel we should be aware of that have affected your educational attainment, then please feel free to tell us about it in your application form. The best way to do this is a short paragraph at the end of your personal statement.